/Engine/Plugins/Experimental/LearningAgents/Source/LearningTraining/Public/LearningPPOTrainer.h
Settings used for training with PPO
struct FPPOTrainerTrainingSettings
float
ActionRegularizationWeight
Weight used to regularize actions.
bool
bAdvantageNormalization
When true, advantages are normalized.
uint32
BatchSize
Batch size to use for training.
bool
bClipAdvantages
When true, very large or small advantages will be clipped.
bool
bUseTensorboard
If to use TensorBoard for logging and tracking the training progress.
ETrainerDevice
Device
Which device to use for training.
float
DiscountFactor
The discount factor causes future rewards to be scaled down so that the policy will favor near-term rewards over potentially uncertain long-term rewards.
float
EntropyWeight
Weighting used for the entropy bonus.
float
EpsilonClip
Clipping ratio to apply to policy updates.
float
GaeLambda
This is used in the Generalized Advantage Estimation as what is essentially an exponential smoothing/decay.
float
InitialActionScale
Initial scale to apply to actions before noise is added to them.
uint32
IterationNum
Number of iterations to train the network for.
float
LearningRateCritic
Learning rate of the critic network.
float
LearningRateDecay
Ratio by which to decay the learning rate every 1000 iterations.
float
LearningRatePolicy
Learning rate of the policy network. Typical values are between 0.001f and 0.0001f.
uint32
Seed
Random Seed to use for training.
int32
TrimEpisodeEndStepNum
Number of steps to trim from the end of each episode during training.
int32
TrimEpisodeStartStepNum
Number of steps to trim from the start of each episode during training.
float
WeightDecay
Amount of weight decay to apply to the network.